Enhancing User Experience (and Behavior): Stanford’s Alexa Prize Team Presents Three Noteworthy Papers
Introduction:
In 2019, Stanford participated in the Amazon Alexa Prize Socialbot Grand Challenge 3 and secured second place with its bot, Chirpy Cardinal. In this post, we delve into the research conducted while developing Chirpy Cardinal, focusing on the common pain points that users face when interacting with socialbots and strategies to address them. The Alexa Prize offers a unique research opportunity, allowing us to study user-bot interactions when users are solely motivated by their own interests. One of the key findings of our research is that neural generative dialogue models often struggle to maintain coherent conversations in real-life settings, resulting in user dissatisfaction. We have also explored effective strategies for handling offensive user behavior, aiming to create a safer and more welcoming environment for users. By training a model to predict user dissatisfaction and implementing response strategies like explicit redirection and empathetic responses, we aim to continuously improve the user experience.
Full Article: Enhancing User Experience (and Behavior): Stanford’s Alexa Prize Team Presents Three Noteworthy Papers
Common User Complaints and Improving Neural Generative Dialogue
During the Alexa Prize Socialbot Grand Challenge 3, Stanford University’s bot Chirpy Cardinal placed 2nd in the competition. As part of the development process, the team conducted research to identify common pain points users encounter when interacting with socialbots and strategies to address them.
Addressing User Dissatisfaction with Neural Generative Models
Neural generative dialogue models, such as DialoGPT, Meena, and BlenderBot, perform well in controlled settings but struggle in real-life environments like the Alexa Prize competition. Users have varying expectations and personalities, and conversations may be affected by background noise. Chirpy Cardinal, which uses a GPT2-medium model, was used to investigate the performance of these models in real-life settings.
To understand how conversations derail, the team identified seven types of errors made by neural generative models, including repetition, redundant questions, unclear utterances, hallucination, ignoring, logical errors, and insulting utterances. After analyzing user conversations, they found that over half (53%) of the neural-generated utterances contained errors.
The challenging environment also caused 22% of user utterances to be incomprehensible to human annotators. This led to basic bot errors like ignoring or providing unclear and repetitive utterances. Redundant questions and logical errors were common, indicating the need for better reasoning and utilization of conversational history by the neural generative models.
To address user dissatisfaction, the team tracked nine ways users express dissatisfaction, such as asking for clarification, criticizing the bot, or ending the conversation. While there is a correlation between bot errors and user dissatisfaction, it is not always straightforward. Users may continue the conversation even after a bot error, especially after logical errors, using it as an opportunity to educate the bot. Some users express dissatisfaction unrelated to bot errors, depending on their expectations regarding appropriate questions from the bot.
The team aimed to predict dissatisfaction and prevent it before it occurs. They trained a model using user conversations to predict the probability of a bot utterance leading to user dissatisfaction. Despite the noisy correlation between bot errors and dissatisfaction, the model successfully found signals indicating potential dissatisfaction. Through human evaluation, they found that the responses chosen by the predictor, which were least likely to cause dissatisfaction, were of better quality compared to randomly chosen responses. This method demonstrates a viable way to continuously improve neural generative dialogue systems through a semi-supervised online learning approach.
Handling Offensive Users
As the popularity of voice assistants grows, offensive language and abuse from users also increase. The team estimated that more than 10% of user conversations with Chirpy Cardinal contained profanity and explicit offensive language. The team conducted a large-scale quantitative evaluation of response strategies against offensive users in real-life scenarios.
After evaluating four response strategies, the team found that politely rejecting the offensive remark and redirecting the conversation to an alternative topic was the most effective strategy. Including the user’s name in the response did not significantly affect the outcome. Politely asking the user about the reason for their offensive remark proved to be effective in reducing future offenses. Empathetic responses were more effective than generic avoidance responses, while counter-attack responses made no difference.
By combining different factors, such as avoidance, using the user’s name, and redirection, the team constructed responses to address offensive remarks. Three metrics were used to measure the effectiveness of these strategies: re-offense (number of conversations with subsequent offensive utterances), conversation length, and user satisfaction.
Conclusion
Through their research on user dissatisfaction and handling offensive users, the Stanford team provided practical insights for chatbot researchers and developers. Understanding the limitations of neural generative models in real-life environments and developing strategies to address user dissatisfaction and offensive behavior can significantly enhance the user experience with socialbots.
Summary: Enhancing User Experience (and Behavior): Stanford’s Alexa Prize Team Presents Three Noteworthy Papers
In 2019, Stanford’s bot Chirpy Cardinal won 2nd place in the Alexa Prize Socialbot Grand Challenge. As part of the research, the team studied user interactions with socialbots and identified common pain points and strategies to address them. They found that neural generative dialogue models like DialoGPT and Meena perform well in controlled settings but struggle in real-life environments. Users expressed dissatisfaction due to bot errors, including repetition, unclear utterances, and logical errors. The team developed a model to predict user dissatisfaction and prevent it mid-conversation. They also addressed offensive user behavior by testing different response strategies and found that politely redirecting the user is the most effective approach.
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